大会报告01——Professor Dr.-Ing. S. X. Ding

Towards control-theoretically explainable diagnosis of faults in dynamic control systems

Professor Dr.-Ing. S. X. Ding

Institute of Automatic Control and Complex Systems (AKS), University of Duisburg-Essen, Germany

        报告摘要：The model-based technique for fault diagnosis (FDI) in dynamic control systems is well-established, although data-driven and machine learning (ML) algorithms build the mainstream in research in recent years. The model-based FDI framework is established on the basis of input-output models of the system under consideration and rigorous application of control-theoretic methods. Differently, data-driven and ML-based algorithms are mainly focused on feature generation from the data space, and on modelling of the considered system operations by means of, technically speaking, identification and optimisation algorithms, known as training and learning as well. In particular, integration of various metrics known in statistics and optimisation theory in the loss functions for training/learning makes data-driven and ML-based algorithms considerably capable for dealing with FDI issues as classification problems.  A critical and obvious barrier for a wide and successful application of data-driven and ML-based algorithms to industrial automatic control systems is their (application) explainability and interpretability.

        报告人简介：Steven X. Ding is a professor of control engineering and the head of the Institute of Automatic Control and Complex Systems (AKS) at the University of Duisburg-Essen, Germany. He received the Dr.-Ing. (Ph.D) degree in electrical engineering from the University of Duisburg in 1992. Between 1992 and 1994, he was with company Rheinmetall GmbH, Germany. From 1995 to 2001, he was a professor of control engineering at the University of Applied Science Lausitz in Senftenberg, Germany, and served as the vice president of this university during 1998 – 2000. Since 2001, he has been a full-professor of control engineering at the University of Duisburg-Essen. Prof. Ding is an independent researcher. His research interests are model-based and data-driven fault diagnosis, process monitoring and control as well as their applications to automotive and process industry, renewable energy, smart building systems as well as secure cyber-physical systems.

大会报告02——Professor Biao Huang, Canadian Academy of Engineering Fellow, IEEE Fellow

Transfer Learning and Its Related FDI

Professor Biao Huang, Canadian Academy of Engineering Fellow, IEEE Fellow

Department of Chemical and Materials Engineering, University of Alberta, Canada

        报告摘要：With advance in computing power and multisensory technology, massive data accumulated can be processed by data-driven techniques to learn underlying driving forces and/or hidden patterns for better monitoring and control of industrial processes. In order to train a data-driven model with satisfactory performance, sizable amounts of labelled data are required. However, it is often expensive, time consuming and labor-intensive to gather well-labelled data although massive data are being collected. It becomes even more demanding to collect such labelled data for an industrial plant under situations such as, for example, when the plant has just started operation, the labels are the faulty events that are rare events, or the labels are lab data that are sampled sparsely. This is commonly referred to as the cold-start problem, where decisions have to be made while little or almost nothing has been known about the environment. In the context of fault detection and diagnosis (FDI), how to transfer the knowledge learned from some existing processes into a related target process that has limited labels to establish a satisfactory FDI is of great practical interest. This presentation will give a systematic overview of transfer learning and its related fault detection and diagnosis along with its perspective.

        报告人简介：Biao Huang obtained his Ph.D. degree in Process Control from the University of Alberta, Canada, in 1997. He had an MSc degree (1986) and a BSc degree (1983) in Automatic Control from the Beijing University of Aeronautics and Astronautics. He joined the University of Alberta in 1997 as an Assistant Professor in the Department of Chemical and Materials Engineering and is currently a Full Professor. He held position of NSERC Senior Industrial Research Chair. He is an IEEE Fellow, Fellow of the Canadian Academy of Engineering, Fellow of the Chemical Institute of Canada, and recipient of many awards, including Germany’s Alexander von Humboldt Research Fellowship, APEGA Summit Award in Research Excellence, ASTech Outstanding Achievement in Science and Engineering Award, R.S. Jane Award and the best paper award from the Journal of Process Control. Biao Huang’s research interests include process data analytics, machine learning, system identification, image processing, fault detection and isolation, and soft sensors. He has published five books and over 500 journal papers.  Biao Huang currently serves as the Editor-in-Chief for IFAC Journal Control Engineering Practice, Subject Editor for Journal of the Franklin Institute, Associate Editor for IEEE/CAA Journal of Automatica Sinica, and Associate Editor for Journal of Process Control.

大会报告03——周东华教授

非平稳工业过程异常监测的研究进展

周东华 教授

山东科技大学副校长、清华大学双聘教授

        报告摘要：非平稳工业过程是普遍存在的，保障其安全平稳运行十分重要，因此，此类系统的异常监测在过去的20多年里得到了学术界的高度重视，已产生了大量方法。本报告介绍了此问题的研究背景和意义，研究现状与主要方法概述，并介绍了本课题组在此领域的一些主要研究进展。最后，对未来的发展方向进行了展望。

        报告人简介：周东华，教授/博导，上海交通大学博士、浙江大学博士后。矿山安全检测技术与自动化装备国家地方联合工程研究中心（青岛）主任、山东科技大学副校长、清华大学双聘教授。曾任清华大学自动化系主任、教育部高等学校自动化类专业教指委主任、国务院控制科学与工程学科评议组成员。为国家杰出青年基金获得者、长江学者特聘教授、国家“万人计划”领军人才、国家基金委创新研究群体带头人，享受国务院政府特殊津贴。兼任IFAC技术过程故障诊断与安全性技术委员会委员、中国自动化学会副理事长、技术过程故障诊断与安全性专委会主任等。主要研究动态系统故障诊断与容错控制、运行安全性评估理论等。以第一完成人获国家自然科学二等奖2项、国家级教学成果二等奖1项、省部级和全国学会科技一等奖4项。曾获全国优秀博士后奖、霍英东教育基金会青年教师奖、第六届中国青年科技奖、国家新世纪百千万人才、全国优秀科技工作者、全国黄大年式教师团队带头人等荣誉称号。为山东省泰山学者优势特色学科人才团队领军人才、泰山学者攀登计划专家，入选全球高被引科学家、全球前2 %顶尖科学家名录，当选IEEE/AAIA/IET/CAA Fellow。